Enhanced Pavement Design and Analysis Framework to Improve the Resiliency of Flexible Airfield Pavements
Bibliographic record
Abstract
Changes in climatic conditions can directly affect pavement performance. However, accounting for environmental factors in airport pavement design remains a challenge since design methods such as FAA rigid and flexible iterative elastic layered design (FAARFIELD) do not consider moisture and temperature variation as input. Therefore, to address this research gap and improve the resilience of airport pavements, this research proposes a new methodology for the structural design of flexible airport pavements. The proposed methodology in this research was applied to a case study of an international airport in Canada, using actual field data. Five scenarios were evaluated including the current climate, temperature increase, lower matric suction, and two flooding events. The results of the proposed design method showed that the traditional FAARFIELD analysis can possibly overestimate fatigue damage, and underestimate rutting damage. The outcomes showed that climate change can increase pavement damage and shorten the service life from 7 to 14 years in the scenarios evaluated. It was also concluded that the lowering of the matric suction can result in the highest damage levels. Considering the implications of climate change on transportation infrastructure, the proposed methodology can contribute to designing more resilient airport pavements in the future, since it accounts for climate variations, temperature, and moisture changes, as well as extreme events such as flooding over the design life of flexible airport pavements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".